안녕하세요! 어떻게 도와드릴까요?

Here's a step-by-step plan for creating the model to classify news articles into one of the eight possible labels.

1. Install necessary libraries and set environment variables:

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!pip install kaggle
!pip install wandb==0.14.0
!pip install pytorch-lightning==1.9.4
!pip install torch==1.13.0

import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"

1. Define my_config:

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my_config = {
    'project_name': 'ChatGPT-prompt',
    'model_name': 'microsoft/deberta-v3-large',
    'num_workers': 4,  # adjust as needed
    'num_labels': 8,
    'max_epochs': 5,
    'max_length': 256,
    'batch_size': 16,
    'accumulate_grad_batches': 4,
    'warmup_steps': None,  # to be set later
    'wandb_api_key': '93460ff86561b201141546a407885ba3c8318d81',
    'data_dir': '/kaggle/input/pp-dataset/',
    'result_dir': 'kaggle/working/result'
}

1. Define dataset class:

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from torch.utils.data import Dataset

class NewsDataset(Dataset):
    def __init__(self, df, tokenizer, max_length, train_or_test='train'):
        self.df = df
        self.tokenizer = tokenizer
        self.max_length = max_length
        self.train_or_test = train_or_test

    def __len__(self):
        return len(self.df)

    def __getitem__(self, idx):
        text = self.df.loc[idx, 'text']
        inputs = self.tokenizer(text, max_length=self.max_length, padding='max_length', truncation=True, return_tensors='pt')

        if self.train_or_test == 'train':
            label = self.df.loc[idx, 'label']
            return {'input_ids': inputs['input_ids'].squeeze(), 'attention_mask': inputs['attention_mask'].squeeze(), 'labels': label}
        else:
            return {'input_ids': inputs['input_ids'].squeeze(), 'attention_mask': inputs['attention_mask'].squeeze()}

1. Define datamodule:

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from pytorch_lightning import LightningDataModule
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
from imblearn.over_sampling import RandomOverSampler

class NewsDataModule(LightningDataModule):
    def __init__(self, config, tokenizer):
        super().__init__()
        self.config = config
        self.tokenizer = tokenizer
        self.batch_size = config['batch_size']
        self.num_workers = config['num_workers']

    def prepare_data(self):
        # Load train and test data
        self.train_df = pd.read_csv(os.path.join(self.config['data_dir'], 'pp_train.csv'))
        self.test_df = pd.read_csv(os.path.join(self.config['data_dir'], 'pp_test.csv'))

    def setup(self, stage=None):
        # Split train data into train and validation
        train_data, val_data = train_test_split(self.train_df, test_size=0.2, random_state=43, stratify=self.train_df['label'])

        # Apply RandomOverSampler
        ros = RandomOverSampler(random_state=43)
        X_resampled, y_resampled = ros.fit_resample(train_data[['id', 'text']], train_data['label'])

        # Create datasets
        self.train_dataset = News

Dataset(X_resampled, self.tokenizer, self.config['max_length'], 'train') self.val_dataset = NewsDataset(val_data, self.tokenizer, self.config['max_length'], 'train') self.test_dataset = NewsDataset(self.test_df, self.tokenizer, self.config['max_length'], 'test')

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def train_dataloader(self):
    return DataLoader(self.train_dataset, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=True)

def val_dataloader(self):
    return DataLoader(self.val_dataset, batch_size=self.batch_size, num_workers=self.num_workers)

def test_dataloader(self):
    return DataLoader(self.test_dataset, batch_size=self.batch_size, num_workers=self.num_workers)

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5. Define PyTorch Lightning classification model:
```python
import pytorch_lightning as pl
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig, AdamW
from sklearn.metrics import f1_score

class NewsClassifier(pl.LightningModule):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.model = AutoModelForSequenceClassification.from_pretrained(config['model_name'], num_labels=config['num_labels'])

    def forward(self, input_ids, attention_mask):
        return self.model(input_ids=input_ids, attention_mask=attention_mask)

    def training_step(self, batch, batch_idx):
        outputs = self(batch['input_ids'], batch['attention_mask'])
        loss = outputs.loss
        return {'loss': loss}

    def validation_step(self, batch, batch_idx):
        outputs = self(batch['input_ids'], batch['attention_mask'])
        loss = outputs.loss
        logits = outputs.logits.argmax(dim=1)
        f1 = f1_score(batch['labels'].cpu(), logits.cpu(), average='macro')
        return {'val_loss': loss, 'val_f1_score': f1}

    def test_step(self, batch, batch_idx):
        outputs = self(batch['input_ids'], batch['attention_mask'])
        logits = outputs.logits.argmax(dim=1)
        return {'id': batch['id'], 'label': logits}

    def test_epoch_end(self, outputs):
        # Generate submission CSV
        ids = []
        labels = []
        for out in outputs:
            ids.extend(out['id'].tolist())
            labels.extend(out['label'].tolist())
        submission = pd.DataFrame({'id': ids, 'label': labels})
        submission.to_csv(os.path.join(self.config['result_dir'], 'submission_debertav3.csv'), index=False)

    def configure_optimizers(self):
        optimizer = AdamW(self.parameters(), lr=2e-5)
        return optimizer

1. Set up training, logging, and callbacks:

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from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint, LearningRateMonitor
from pytorch_lightning.loggers import WandbLogger

# Create tokenizer
tokenizer = AutoTokenizer.from_pretrained(my_config['model_name'])

# Create dataset
dm = NewsDataModule(my_config, tokenizer)
dm.prepare_data()
dm.setup()

# Set warmup_steps
my_config['warmup_steps'] = (len(dm.train_dataloader()) * my_config['max_epochs']) // 5

# Set up logger and callbacks
wandb_logger = WandbLogger(project=my_config['project_name'], api_key=my_config['wandb_api_key'])
early_stopping = EarlyStopping(monitor='val_f1_score', mode='max', patience=1)
checkpoint_callback = ModelCheckpoint(monitor='val_f1_score', mode='max', save_top_k=1, dirpath=my_config['result_dir'], filename='best_model')
lr_monitor = LearningRateMonitor(logging_interval='step

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7. Set up Trainer and start training:

```python
# Create Trainer
trainer = pl.Trainer(
    logger=wandb_logger,
    gpus=1,
    max_epochs=my_config['max_epochs'],
    callbacks=[early_stopping, checkpoint_callback, lr_monitor],
    precision=16,
    accumulate_grad_batches=my_config['accumulate_grad_batches'],
    stochastic_weight_avg=True
)

# Train the model
news_classifier = NewsClassifier(my_config)
trainer.fit(news_classifier, dm)

# Test the model
trainer.test(news_classifier, datamodule=dm)

With this setup, you can train a DeBERTa-v3 model for classifying news articles using the given dataset. This will also generate the submission file, submission_debertav3.csv, with the predicted labels for the test dataset.

Keep in mind that this is just one example, and there are many ways to fine-tune and optimize the model, such as by adjusting hyperparameters, experimenting with different model architectures, or using additional techniques like transfer learning or data augmentation.

It seems like the issue is occurring in the __getitem__ method of the NewsDataset class when trying to access the dataframe using self.df.loc[idx, 'text']. This error may be caused by the DataFrame not being indexed properly. To resolve this issue, you can reset the index of the DataFrame before using it in the Dataset class.

In the NewsDataModule class, after applying the RandomOverSampler and creating the new resampled DataFrame, reset the index for X_resampled, val_data, and self.test_df:

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X_resampled.reset_index(drop=True, inplace=True)
val_data.reset_index(drop=True, inplace=True)
self.test_df.reset_index(drop=True, inplace=True)

Add these lines of code inside the setup method of the NewsDataModule class after the RandomOverSampler:

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def setup(self, stage=None):
    # Split train data into train and validation
    train_data, val_data = train_test_split(self.train_df, test_size=0.2, random_state=43, stratify=self.train_df['label'])

    # Apply RandomOverSampler
    ros = RandomOverSampler(random_state=43)
    X_resampled, y_resampled = ros.fit_resample(train_data[['id', 'text']], train_data['label'])

    # Reset the index
    X_resampled.reset_index(drop=True, inplace=True)
    val_data.reset_index(drop=True, inplace=True)
    self.test_df.reset_index(drop=True, inplace=True)

    # Create datasets
    ...

This should resolve the KeyError and allow the DataLoader to access the data correctly.

데이터 프레임의 인덱스 문제로 인한 KeyError가 발생했기 때문에, 인덱스를 재설정하는 과정이 필요합니다. 데이터 전처리 및 샘플링 과정에서 원본 데이터의 인덱스가 변경되거나 누락될 수 있습니다. 이로 인해 DataLoader가 데이터를 올바르게 읽지 못할 수 있습니다.

인덱스를 재설정하는 이유는 다음과 같습니다:

1. 데이터 프레임을 올바르게 읽기 위해: __getitem__ 메소드에서 self.df.loc[idx, 'text']를 사용하여 데이터 프레임의 특정 행을 가져오려고 했습니다. 그러나 데이터 프레임의 인덱스가 불연속적이거나 변경되었기 때문에 KeyError가 발생했습니다. 인덱스를 재설정하면 이러한 문제를 해결하고 DataLoader가 데이터를 올바르게 읽을 수 있습니다.

2. 데이터 샘플링 과정에서 인덱스 불일치 방지: 데이터를 분할하거나 오버 샘플링을 사용하면 원본 데이터 프레임의 인덱스가 변경되거나 일부 인덱스가 누락될 수 있습니다. 이로 인해 인덱스가 불연속적이거나 일치하지 않을 수 있습니다. 인덱스를 재설정하면 데이터 프레임의 인덱스가 연속적이고 일관된 상태가 유지되어 이후의 데이터 처리 과정에서 문제가 발생하지 않습니다.

요약하면, 인덱스를 재설정하는 과정은 데이터 프레임의 인덱스 문제를 해결하고 DataLoader가 데이터를 올바르게 읽을 수 있도록 하는 데 필요합니다. 이 과정은 데이터 전처리 및 샘플링 과정에서 발생할 수 있는 인덱스 불일치 문제를 해결하는 데 도움이 됩니다.

This KeyError suggests that the 'label' column is missing in the DataFrame when it is trying to be accessed. It is important to ensure that the 'label' column is available in the DataFrame when it is required.

Looking back at the code, I see that this issue may occur during the test dataset processing. In the NewsDataModule class, you've mentioned that the test dataset doesn't have a 'label' column, but you're not handling this situation in the NewsDataset class.

To fix this issue, you need to modify the __getitem__ method of the NewsDataset class to handle the case when the 'label' column is not available (i.e., when processing the test dataset).

Here's an updated version of the __getitem__ method:

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def __getitem__(self, idx):
    text = self.df.loc[idx, 'text']
    input_ids, attention_mask = self.tokenize(text)

    if self.train_or_test == 'test':
        return {
            'id': self.df.loc[idx, 'id'],
            'input_ids': torch.tensor(input_ids, dtype=torch.long),
            'attention_mask': torch.tensor(attention_mask, dtype=torch.long)
        }
    else:
        label = self.df.loc[idx, 'label']
        return {
            'input_ids': torch.tensor(input_ids, dtype=torch.long),
            'attention_mask': torch.tensor(attention_mask, dtype=torch.long),
            'labels': torch.tensor(label, dtype=torch.long)
        }

With this update, the code checks whether it is processing the test dataset or not. If it's the test dataset, it doesn't try to access the 'label' column and returns only the 'id', 'input_ids', and 'attention_mask'. Otherwise, it returns 'input_ids', 'attention_mask', and 'labels' as before. This should resolve the KeyError.

You're right. I apologize for the oversight. After resampling the data using the RandomOverSampler, you need to concatenate X_resampled and y_resampled to create a new DataFrame with both the text and label columns. You can do this using the pd.concat function:

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# Concatenate X_resampled and y_resampled
resampled_data = pd.concat([X_resampled, y_resampled], axis=1)

Then, update the DataFrame passed to the NewsDataset class for the train dataset:

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self.train_dataset = NewsDataset(resampled_data, self.tokenizer, self.config['max_length'], 'train')

Here's the updated setup method for the NewsDataModule class:

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def setup(self, stage=None):
    # Split train data into train and validation
    train_data, val_data = train_test_split(self.train_df, test_size=0.2, random_state=43, stratify=self.train_df['label'])

    # Apply RandomOverSampler
    ros = RandomOverSampler(random_state=43)
    X_resampled, y_resampled = ros.fit_resample(train_data[['id', 'text']], train_data['label'])

    # Concatenate X_resampled and y_resampled
    resampled_data = pd.concat([X_resampled, y_resampled], axis=1)

    # Reset the index
    resampled_data.reset_index(drop=True, inplace=True)
    val_data.reset_index(drop=True, inplace=True)
    self.test_df.reset_index(drop=True, inplace=True)

    # Create datasets
    self.train_dataset = NewsDataset(resampled_data, self.tokenizer, self.config['max_length'], 'train')
    self.val_dataset = NewsDataset(val_data, self.tokenizer, self.config['max_length'], 'valid')
    self.test_dataset = NewsDataset(self.test_df, self.tokenizer, self.config['max_length'], 'test')

Now, the train dataset should have both the text and label columns, which should resolve any issues when creating and using the NewsDataset object.

The OutOfMemoryError indicates that your model is running out of GPU memory during training. This can be caused by the model size, batch size, or input sequence length. There are several ways to address this issue:

1. Reduce the batch size: Lowering the batch size will reduce the GPU memory consumption, but it may also affect the model's performance. Update the my_config dictionary with a smaller batch size:

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my_config = {
    ...
    'batch_size': 8,  # or even lower, depending on your GPU memory
    ...
}

1. Reduce the maximum input sequence length: If your input sequences are very long, you can truncate or reduce the maximum input sequence length to save GPU memory. Update the my_config dictionary with a smaller max length:

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my_config = {
    ...
    'max_length': 128,  # or an appropriate length based on your data
    ...
}

1. Enable mixed-precision training: Using mixed-precision training can help reduce memory consumption without significantly affecting model performance. Update the Trainer initialization with the precision argument:

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trainer = pl.Trainer(
    ...
    precision=16,  # Enables mixed-precision training
    ...
)

1. Gradient accumulation: You can accumulate gradients over multiple batches before updating the model weights. This allows you to effectively increase the batch size without increasing memory consumption. Update the my_config dictionary with the appropriate accumulate_grad_batches value:

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my_config = {
    ...
    'accumulate_grad_batches': 8,  # or a different value, depending on your needs
    ...
}

1. Use a smaller model: If you still face memory issues, consider using a smaller version of the model. For example, you can try using microsoft/deberta-v3-base instead of microsoft/deberta-v3-large. Update the my_config dictionary with the smaller model:

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my_config = {
    ...
    'classification_model_name': 'microsoft/deberta-v3-base',
    ...
}

Try these approaches one by one or in combination until your model fits within the available GPU memory. Keep in mind that making changes to the configuration may affect the performance of your model, so you may need to fine-tune the hyperparameters accordingly.

This error occurs because PyTorch Lightning expects a key called 'loss' in the dictionary returned by the training_step method when using automatic optimization. To fix this issue, simply change the key 'loss' in the dictionary returned by training_step.

Here's an example of how to update the training_step method in your model:

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def training_step(self, batch, batch_idx):
    outputs = self(**batch)
    loss = outputs.loss
    self.log('train_loss', loss, prog_bar=True, on_step=True, on_epoch=True)
    return {'loss': loss}  # Make sure to use the key 'loss' here

By returning a dictionary with the key 'loss', PyTorch Lightning will recognize the loss value correctly and perform automatic optimization as expected.

I apologize for the confusion. In that case, let's review your training_step method to identify any issues. Please share the code for the training_step method in your PyTorch Lightning model. I'll be able to provide further guidance after examining your implementation.

My apologies for the confusion earlier. Your training_step method looks correct.

However, there's a possibility that the loss attribute might not be available in the outputs object. This can happen if you're not passing the labels to your model during training. When using Hugging Face transformers, you need to pass the labels to the model so that it can calculate the loss internally.

Here's the updated training_step method:

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def training_step(self, batch, batch_idx):
    outputs = self(
        input_ids=batch['input_ids'],
        attention_mask=batch['attention_mask'],
        labels=batch['labels']  # Pass the labels to the model
    )
    loss = outputs.loss
    return {'loss': loss}

By passing the labels to the model, it should now be able to calculate the loss and return it in the outputs object.

Thank you for sharing the code. I noticed that the labels are not being passed to the model in the forward method. You should update the forward method to include the labels argument and pass it to the model. Here's the updated code:

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class NewsClassifier(pl.LightningModule):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.model = AutoModelForSequenceClassification.from_pretrained(config['model_name'], num_labels=config['num_labels'])

    def forward(self, input_ids, attention_mask, labels=None):
        return self.model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)

    def training_step(self, batch, batch_idx):
        outputs = self(batch['input_ids'], batch['attention_mask'], batch['labels'])
        loss = outputs.loss
        return {'loss': loss}

    ...

By including the labels in the forward method, the model should now be able to calculate the loss and return it in the outputs object.

You're welcome! If you have any more questions or need further assistance, feel free to ask. Good luck with your project!

Sure, I can help you make those changes. Here's the updated my_config dictionary and the configure_optimizers method in the NewsClassifier class:

1. Update the learning rate in my_config:

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my_config = {
    ...
    'lr': 6e-6,  # Set learning rate to 6e-6
    'batch_size': 4,  # Set batch size to 4
    ...
}

1. Update the configure_optimizers method in the NewsClassifier class to use the learning rate from my_config:

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class NewsClassifier(pl.LightningModule):
    ...
    def configure_optimizers(self):
        optimizer = AdamW(self.parameters(), lr=self.config['lr'])
        return optimizer

By making these changes, your model will now use a learning rate of 6e-6 and a batch size of 4 during training.

Sure, I'll modify the test_epoch_end method to move the label to the CPU before generating the CSV file. Here's the updated code:

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def test_epoch_end(self, outputs):
    # Generate submission CSV
    ids = []
    labels = []
    for out in outputs:
        ids.extend(out['id'])
        labels.extend(out['label'].cpu().tolist())  # Move the label to the CPU before converting to a list
    submission = pd.DataFrame({'id': ids, 'label': labels})
    submission.to_csv(os.path.join(self.config['result_dir'], 'submission_debertav3.csv'), index=False)

By updating the test_epoch_end method in this way, the label will be moved to the CPU before being converted to a list and used for generating the CSV file.